MUSA550 Geospatial Data Science in Python¶

Assignment 3¶

Hang Zhao¶

In [1]:
import pandas as pd
import geopandas as gpd
import numpy as np

# See lots of columns
pd.options.display.max_columns = 999

# Hide warnings due to issue in shapely package 
# See: https://github.com/shapely/shapely/issues/1345
np.seterr(invalid="ignore");

Assignment 3¶

This assignment will contain two parts:

  1. Exploring evictions and code violations in Philadelphia
  2. Comparing the NDVI in Philadelphia

Part 1: Exploring Evictions and Code Violations in Philadelphia¶

In this assignment, we'll explore spatial trends evictions in Philadelphia using data from the Eviction Lab and building code violations using data from OpenDataPhilly.

We'll be exploring the idea that evictions can occur as retaliation against renters for reporting code violations. Spatial correlations between evictions and code violations from the City's Licenses and Inspections department can offer some insight into this question.

A couple of interesting background readings:

  • HuffPost article
  • PlanPhilly article

1.1 Explore Eviction Lab Data¶

The Eviction Lab built the first national database for evictions. If you aren't familiar with the project, you can explore their website: https://evictionlab.org/

1.1.1 Read data using geopandas¶

The first step is to read the eviction data by census tract using geopandas. The data for all of Pennsylvania by census tract is available in the data/ folder in a GeoJSON format.

Load the data file "PA-tracts.geojson" using geopandas

Note: If you'd like to see all columns in the data frame, you can increase the max number of columns using pandas display options:

In [2]:
PA_Census = gpd.read_file("data/PA-tracts.geojson")

1.1.2 Explore and trim the data¶

We will need to trim data to Philadelphia only. Take a look at the data dictionary for the descriptions of the various columns in top-level repository folder: eviction_lab_data_dictionary.txt

Note: the column names are shortened — see the end of the above file for the abbreviations. The numbers at the end of the columns indicate the years. For example, e-16 is the number of evictions in 2016.

Take a look at the individual columns and trim to census tracts in Philadelphia. (Hint: Philadelphia is both a city and a county).

In [3]:
philly = PA_Census.loc[PA_Census['pl'] == 'Philadelphia County, Pennsylvania']

1.1.3 Transform from wide to tidy format¶

For this assignment, we are interested in the number of evictions by census tract for various years. Right now, each year has it's own column, so it will be easiest to transform to a tidy format.

Use the pd.melt() function to transform the eviction data into tidy format, using the number of evictions from 2003 to 2016.

The tidy data frame should have four columns: GEOID, geometry, a column holding the number of evictions, and a column telling you what the name of the original column was for that value.

Hints:

  • You'll want to specify the GEOID and geometry columns as the id_vars. This will keep track of the census tract information.
  • You should specify the names of the columns holding the number of evictions as the value_vars.
  • You can generate a list of this column names using Python's f-string formatting:
    value_vars = [f"e-{x:02d}" for x in range(3, 17)]
In [4]:
value_vars = [f"e-{x:02d}" for x in range(3, 17)]
philly = philly.melt(id_vars=['GEOID','geometry'], value_vars=value_vars, var_name='year', value_name='evictions')
In [5]:
philly.head(n=10)
Out[5]:
GEOID geometry year evictions
0 42101000100 MULTIPOLYGON (((-75.14161 39.95549, -75.14163 ... e-03 21.0
1 42101000200 MULTIPOLYGON (((-75.15122 39.95686, -75.15167 ... e-03 3.0
2 42101000300 MULTIPOLYGON (((-75.16234 39.95782, -75.16237 ... e-03 17.0
3 42101000801 MULTIPOLYGON (((-75.17732 39.95096, -75.17784 ... e-03 13.0
4 42101000804 MULTIPOLYGON (((-75.17118 39.94778, -75.17102 ... e-03 21.0
5 42101001002 MULTIPOLYGON (((-75.14919 39.94903, -75.14602 ... e-03 16.0
6 42101001400 MULTIPOLYGON (((-75.16558 39.94366, -75.16567 ... e-03 30.0
7 42101002100 MULTIPOLYGON (((-75.18062 39.93582, -75.17984 ... e-03 8.0
8 42101018800 MULTIPOLYGON (((-75.10971 39.99233, -75.11026 ... e-03 75.0
9 42101019000 MULTIPOLYGON (((-75.09704 40.01602, -75.09656 ... e-03 43.0

1.1.4 Plot the total number of evictions per year from 2003 to 2016¶

Use hvplot to plot the total number of evictions from 2003 to 2016. You will first need to perform a group by operation and sum up the total number of evictions for all census tracts, and then use hvplot() to make your plot.

You can use any type of hvplot chart you'd like to show the trend in number of evictions over time.

In [6]:
import holoviews as hv
import hvplot.pandas
hv.extension("bokeh")
In [7]:
philly_grouped = philly.groupby("year", as_index=False)["evictions"].sum()
philly_grouped
Out[7]:
year evictions
0 e-03 10647.0
1 e-04 10491.0
2 e-05 10550.0
3 e-06 11078.0
4 e-07 11032.0
5 e-08 10866.0
6 e-09 9821.0
7 e-10 10628.0
8 e-11 10882.0
9 e-12 11130.0
10 e-13 10803.0
11 e-14 11182.0
12 e-15 10098.0
13 e-16 10264.0
In [8]:
philly_grouped.hvplot(x='year', y='evictions', kind="line")
Out[8]:

1.1.5 The number of evictions across Philadelphia¶

Our tidy data frame is still a GeoDataFrame with a geometry column, so we can visualize the number of evictions for all census tracts.

Use hvplot() to generate a choropleth showing the number of evictions for a specified year, with a widget dropdown to select a given year (or variable name, e.g., e-16, e-15, etc).

Hints

  • You'll need to use the groupby keyword to tell hvplot to make a series of maps, with a widget to select between them.
  • You will need to specify dynamic=False as a keyword argument to the hvplot() function.
  • Be sure to specify a width and height that makes your output map (roughly) square to limit distortions
In [9]:
# Via hvplot

choro = philly.to_crs(epsg=3857).hvplot(
    c="evictions",
    frame_width=600,
    frame_height=600,
    groupby='year',
    alpha=0.7,
    geo=True,
    crs=3857,
    cmap="inferno",
)
choro
# gvts.EsriImagery * choro
Out[9]:

1.2 Code Violations in Philadelphia¶

Next, we'll explore data for code violations from the Licenses and Inspections Department of Philadelphia to look for potential correlations with the number of evictions.

1.2.1 Load data from 2012 to 2016¶

L+I violation data for years including 2012 through 2016 (inclusive) is provided in a CSV format in the "data/" folder.

Load the data using pandas and convert to a GeoDataFrame.

In [10]:
# Load the data from a CSV file into a pandas DataFrame
violation_df = pd.read_csv(
    "data/li_violations.csv"  # Use the file path relative to the current folder
)

# Remove rows with missing geometry
violation_df = violation_df.dropna(subset=["lat", "lng"])

# Create our GeoDataFrame with geometry column created from lon/lat
violation = gpd.GeoDataFrame(
    violation_df,
    geometry=gpd.points_from_xy(violation_df["lng"], violation_df["lat"]),
    crs="EPSG:4326",
)
In [11]:
violation_0 = gpd.GeoDataFrame(
    violation_df, geometry=gpd.points_from_xy(violation_df.lng, violation_df.lat), crs="EPSG:4326"
)
violation_0
Out[11]:
lat lng violationdescription geometry
0 40.050526 -75.126076 CLIP VIOLATION NOTICE POINT (-75.12608 40.05053)
1 40.050593 -75.126578 LICENSE-CHANGE OF ADDRESS POINT (-75.12658 40.05059)
2 40.050593 -75.126578 LICENSE-RES SFD/2FD POINT (-75.12658 40.05059)
3 39.991994 -75.128895 EXT A-CLEAN WEEDS/PLANTS POINT (-75.12889 39.99199)
4 40.023260 -75.164848 EXT A-VACANT LOT CLEAN/MAINTAI POINT (-75.16485 40.02326)
... ... ... ... ...
434047 40.012805 -75.155963 SD-REQD EXIST GROUP R POINT (-75.15596 40.01281)
434048 40.009985 -75.068968 RUBBISH/GARBAGE EXTERIOR-OWNER POINT (-75.06897 40.00999)
434049 40.009829 -75.068912 CLIP VIOLATION NOTICE POINT (-75.06891 40.00983)
434050 40.009776 -75.068895 PERSONAL PROPERTY EXT OWNER POINT (-75.06889 40.00978)
434051 40.009776 -75.068895 LICENSE - RENTAL PROPERTY POINT (-75.06889 40.00978)

434052 rows × 4 columns

1.2.2 Trim to specific violation types¶

There are many different types of code violations (running the nunique() function on the violationdescription column will extract all of the unique ones). More information on different types of violations can be found on the City's website.

Below, I've selected 15 types of violations that deal with property maintenance and licensing issues. We'll focus on these violations. The goal is to see if these kinds of violations are correlated spatially with the number of evictions in a given area.

Use the list of violations given to trim your data set to only include these types.

In [12]:
violation['violationdescription'].nunique()
Out[12]:
1342
In [13]:
violation_types = [
    "INT-PLMBG MAINT FIXTURES-RES",
    "INT S-CEILING REPAIR/MAINT SAN",
    "PLUMBING SYSTEMS-GENERAL",
    "CO DETECTOR NEEDED",
    "INTERIOR SURFACES",
    "EXT S-ROOF REPAIR",
    "ELEC-RECEPTABLE DEFECTIVE-RES",
    "INT S-FLOOR REPAIR",
    "DRAINAGE-MAIN DRAIN REPAIR-RES",
    "DRAINAGE-DOWNSPOUT REPR/REPLC",
    "LIGHT FIXTURE DEFECTIVE-RES",
    "LICENSE-RES SFD/2FD",
    "ELECTRICAL -HAZARD",
    "VACANT PROPERTIES-GENERAL",
    "INT-PLMBG FIXTURES-RES",
]
violation_sel = violation_0['violationdescription'].isin(violation_types)
violation_15 = violation_0.loc[violation_sel]

1.2.3 Make a hex bin map¶

The code violation data is point data. We can get a quick look at the geographic distribution using matplotlib and the hexbin() function. Make a hex bin map of the code violations and overlay the census tract outlines.

Hints:

  • The eviction data from part 1 was by census tract, so the census tract geometries are available as part of that GeoDataFrame. You can use it to overlay the census tracts on your hex bin map.
  • Make sure you convert your GeoDataFrame to a CRS that's better for visualization than plain old 4326.
In [14]:
violation_3857 = violation_15.to_crs(epsg=3857)
In [15]:
from matplotlib import pyplot as plt
In [16]:
# Create the axes
fig, ax = plt.subplots(figsize=(12, 12))

# Extract out the x/y coordindates of the Point objects
xcoords = violation_3857.geometry.x
ycoords = violation_3857.geometry.y

# Plot a hexbin chart
# NOTE: We are passing the full set of coordinates to matplotlib
hex_vals = ax.hexbin(xcoords, ycoords, gridsize=50)


# Add the philly census tract geometry boundaries
philly.to_crs(violation_3857.crs).plot(
    ax=ax, facecolor="none", edgecolor="white", linewidth=0.25
)

# Add a colorbar and format
fig.colorbar(hex_vals, ax=ax)
ax.set_axis_off()
ax.set_aspect("equal")
No description has been provided for this image

1.2.4 Spatially join data sets¶

To do a census tract comparison to our eviction data, we need to find which census tract each of the code violations falls into. Use the geopandas.sjoin() function to do just that.

Hints

  • You can re-use your eviction data frame, but you will only need the geometry column (specifying census tract polygons) and the GEOID column (specifying the name of each census tract).
  • Make sure both data frames have the same CRS before joining them together!
In [17]:
violation_tract = gpd.sjoin(
    violation_3857,  
    philly.to_crs(violation_3857.crs),  
    predicate="intersects",
    how="inner",
).drop(columns=["index_right", "lat", "evictions", "lng", "year"])
violation_tract.head()
Out[17]:
violationdescription geometry GEOID
2 LICENSE-RES SFD/2FD POINT (-8363052.408 4873297.042) 42101027100
16024 CO DETECTOR NEEDED POINT (-8363202.689 4872597.863) 42101027100
17225 LICENSE-RES SFD/2FD POINT (-8363040.274 4872575.906) 42101027100
43436 INT S-CEILING REPAIR/MAINT SAN POINT (-8362971.479 4872591.029) 42101027100
43452 EXT S-ROOF REPAIR POINT (-8362970.588 4872596.991) 42101027100

1.2.5 Calculate the number of violations by type per census tract¶

Next, we'll want to find the number of violations (for each kind) per census tract. You should group the data frame by violation type and census tract name.

The result of this step should be a data frame with three columns: violationdescription, GEOID, and N, where N is the number of violations of that kind in the specified census tract.

Optional: to make prettier plots

Some census tracts won't have any violations, and they won't be included when we do the above calculation. However, there is a trick to set the values for those census tracts to be zero. After you calculate the sizes of each violation/census tract group, you can run:

N = N.unstack(fill_value=0).stack().reset_index(name='N')

where N gives the total size of each of the groups, specified by violation type and census tract name.

See this StackOverflow post for more details.

This part is optional, but will make the resulting maps a bit prettier.

In [18]:
Nviolation_tract = violation_tract.groupby(["violationdescription","GEOID"]).size()
Nviolation_tract = Nviolation_tract.unstack(fill_value=0).stack().reset_index(name='N')
In [19]:
#merged[merged['GEOID']=='42101980800']

1.2.6 Merge with census tracts geometries¶

We now have the number of violations of different types per census tract specified as a regular DataFrame. You can now merge it with the census tract geometries (from your eviction data GeoDataFrame) to create a GeoDataFrame.

Hints

  • Use pandas.merge() and specify the on keyword to be the column holding census tract names.
  • Make sure the result of the merge operation is a GeoDataFrame — you will want the GeoDataFrame holding census tract geometries to be the first argument of the pandas.merge() function.
In [20]:
# Do GeoDataFrame.merge(DataFrame) here...
philly_projected = philly.to_crs(violation_3857.crs)
philly_projected=philly_projected[['GEOID','geometry']].drop_duplicates()
merged = philly_projected.merge(Nviolation_tract, on="GEOID")
merged
Out[20]:
GEOID geometry violationdescription N
0 42101000100 MULTIPOLYGON (((-8364725.429 4859476.459, -836... CO DETECTOR NEEDED 0
1 42101000100 MULTIPOLYGON (((-8364725.429 4859476.459, -836... DRAINAGE-DOWNSPOUT REPR/REPLC 84
2 42101000100 MULTIPOLYGON (((-8364725.429 4859476.459, -836... DRAINAGE-MAIN DRAIN REPAIR-RES 0
3 42101000100 MULTIPOLYGON (((-8364725.429 4859476.459, -836... ELEC-RECEPTABLE DEFECTIVE-RES 0
4 42101000100 MULTIPOLYGON (((-8364725.429 4859476.459, -836... ELECTRICAL -HAZARD 14
... ... ... ... ...
5530 42101018400 MULTIPOLYGON (((-8355531.552 4864854.203, -835... INTERIOR SURFACES 28
5531 42101018400 MULTIPOLYGON (((-8355531.552 4864854.203, -835... LICENSE-RES SFD/2FD 154
5532 42101018400 MULTIPOLYGON (((-8355531.552 4864854.203, -835... LIGHT FIXTURE DEFECTIVE-RES 0
5533 42101018400 MULTIPOLYGON (((-8355531.552 4864854.203, -835... PLUMBING SYSTEMS-GENERAL 28
5534 42101018400 MULTIPOLYGON (((-8355531.552 4864854.203, -835... VACANT PROPERTIES-GENERAL 0

5535 rows × 4 columns

1.2.7 Interactive choropleths for each violation type¶

Now, we can use hvplot() to create an interactive choropleth for each violation type and add a widget to specify different violation types.

Hints

  • You'll need to use the groupby keyword to tell hvplot to make a series of maps, with a widget to select different violation types.
  • You will need to specify dynamic=False as a keyword argument to the hvplot() function.
  • Be sure to specify a width and height that makes your output map (roughly) square to limit distortions
In [21]:
# Via hvplot

choro2 = merged.to_crs(epsg=3857).hvplot(
    c="N",
    frame_width=600,
    frame_height=600,
    groupby='violationdescription',
    alpha=0.7,
    geo=True,
    crs=3857,
    cmap="magma",
    #hover_cols=["GEOID"]
)
choro2
Out[21]:

1.3. A side-by-side comparison¶

From the interactive maps of evictions and violations, you should notice a lot of spatial overlap.

As a final step, we'll make a side-by-side comparison to better show the spatial correlations. This will involve a few steps:

  1. Trim the evictions data frame plotted in section 1.1.5 to only include evictions from 2016.
  2. Trim the L+I violations data frame plotted in section 1.2.7 to only include a single violation type (pick whichever one you want!).
  3. Use hvplot() to make two interactive choropleth maps, one for the data from step 1. and one for the data in step 2.
  4. Show these two plots side by side (one row and 2 columns) using the syntax for combining charts.

Note: since we selected a single year and violation type, you won't need to use the groupby= keyword here.

In [22]:
eviction_e16 = philly["year"].isin(["e-16"])
philly_trim16 = philly.loc[eviction_e16]
vacant_properties = merged["violationdescription"].isin(["INT S-FLOOR REPAIR"])
merged_trim = merged.loc[vacant_properties]
In [23]:
philly_trim16.to_crs(epsg=3857).hvplot(
    c="evictions",
    frame_width=600,
    frame_height=600,
    alpha=0.7,
    geo=True,
    crs=3857,
    cmap="inferno"
) + merged_trim.to_crs(epsg=3857).hvplot(
    c="N",
    frame_width=600,
    frame_height=600,
    alpha=0.7,
    geo=True,
    crs=3857,
    cmap="inferno"
)
Out[23]:

1.4. Extra Credit¶

Identify the 20 most common types of violations within the time period of 2012 to 2016 and create a set of interactive choropleths similar to what was done in section 1.2.7.

Use this set of maps to identify 3 types of violations that don't seem to have much spatial overlap with the number of evictions in the City.

In [24]:
violation_total = violation_0.groupby(["violationdescription"], as_index=False).size()
sort = violation_total.sort_values(by='size', ascending=False)
selected_column = sort.head(n=20)
violation_selected = violation_0['violationdescription'].isin(selected_column['violationdescription'])
violation_20 = violation_0.loc[violation_selected]
violation_20.to_crs(epsg=3857)
Out[24]:
lat lng violationdescription geometry
0 40.050526 -75.126076 CLIP VIOLATION NOTICE POINT (-8362996.526 4873287.299)
2 40.050593 -75.126578 LICENSE-RES SFD/2FD POINT (-8363052.408 4873297.042)
3 39.991994 -75.128895 EXT A-CLEAN WEEDS/PLANTS POINT (-8363310.335 4864778.938)
4 40.023260 -75.164848 EXT A-VACANT LOT CLEAN/MAINTAI POINT (-8367312.605 4869322.935)
5 40.023260 -75.164848 EXT A-VACANT LOT CLEAN/MAINTAI POINT (-8367312.605 4869322.935)
... ... ... ... ...
434044 39.936179 -75.192078 SD-REQD EXIST GROUP R POINT (-8370343.835 4856672.315)
434047 40.012805 -75.155963 SD-REQD EXIST GROUP R POINT (-8366323.531 4867803.242)
434048 40.009985 -75.068968 RUBBISH/GARBAGE EXTERIOR-OWNER POINT (-8356639.292 4867393.379)
434049 40.009829 -75.068912 CLIP VIOLATION NOTICE POINT (-8356633.058 4867370.706)
434051 40.009776 -75.068895 LICENSE - RENTAL PROPERTY POINT (-8356631.166 4867363.003)

235542 rows × 4 columns

In [26]:
violation_join = gpd.sjoin(
    violation_20,  
    philly.to_crs(violation_20.crs),  
    predicate="within",
    how="left",
).drop(columns=["index_right", "lat", "evictions", "lng", "year"])
In [27]:
violation_size = violation_join.groupby(["violationdescription","GEOID"]).size()
violation_size = violation_size.unstack(fill_value=0).stack().reset_index(name='N')
violation_size
Out[27]:
violationdescription GEOID N
0 ANNUAL CERT FIRE ALARM 42101000100 770
1 ANNUAL CERT FIRE ALARM 42101000200 532
2 ANNUAL CERT FIRE ALARM 42101000300 378
3 ANNUAL CERT FIRE ALARM 42101000401 168
4 ANNUAL CERT FIRE ALARM 42101000402 462
... ... ... ...
7655 VIOL C&I MESSAGE 42101980600 42
7656 VIOL C&I MESSAGE 42101980700 658
7657 VIOL C&I MESSAGE 42101980800 14
7658 VIOL C&I MESSAGE 42101980900 294
7659 VIOL C&I MESSAGE 42101989100 448

7660 rows × 3 columns

In [28]:
vp_merged = philly_projected.merge(violation_size, on="GEOID")
In [29]:
choro_total = vp_merged.to_crs(epsg=3857).hvplot(
    c="N",
    frame_width=600,
    frame_height=600,
    groupby='violationdescription',
    alpha=0.7,
    geo=True,
    crs=3857,
    cmap="magma",
    #hover_cols=["GEOID"]
)
choro_total
Out[29]:
In [30]:
print("3 types of violations that don’t seem to have much spatial overlap with the number of evictions in the City:", 
      "\nVIOL C&I MESSAGE",
      "\nRUBBISH/GARBAGE EXTERIOR-OWNER",
      "\nLICENSE-RES GENERAL")

3 types of violations that don’t seem to have much spatial overlap with the number of evictions in the City: 
VIOL C&I MESSAGE 
RUBBISH/GARBAGE EXTERIOR-OWNER 
LICENSE-RES GENERAL

Part 2: Exploring the NDVI in Philadelphia¶

In this part, we'll explore the NDVI in Philadelphia a bit more. This part will include two parts:

  1. We'll compare the median NDVI within the city limits and the immediate suburbs
  2. We'll calculate the NDVI around street trees in the city.

2.1 Comparing the NDVI in the city and the suburbs¶

2.1.1 Load Landsat data for Philadelphia¶

Use rasterio to load the landsat data for Philadelphia (available in the "data/" folder)

In [31]:
import rasterio as rio
In [32]:
# Open the file and get a file "handle"
landsat = rio.open("./data/landsat8_philly.tif")

landsat
Out[32]:
<open DatasetReader name='./data/landsat8_philly.tif' mode='r'>
In [33]:
# The CRS
landsat.crs
Out[33]:
CRS.from_epsg(32618)

2.1.2 Separating the city from the suburbs¶

Create two polygon objects, one for the city limits and one for the suburbs. To calculate the suburbs polygon, we will take everything outside the city limits but still within the bounding box.

  • The city limits are available in the "data/" folder.
  • To calculate the suburbs polygon, the "envelope" attribute of the city limits geometry will be useful.
  • You can use geopandas' geometric manipulation functionality to calculate the suburbs polygon from the city limits polygon and the envelope polygon.
In [34]:
from rasterio.mask import mask
import matplotlib.colors as mcolors
In [35]:
city_limits = gpd.read_file("./data/City_Limits.geojson")
city_limits = city_limits.to_crs(epsg=landsat.crs.to_epsg())
In [36]:
envelope = city_limits.envelope
In [37]:
suburbs = envelope.difference(city_limits)

2.1.3 Mask and calculate the NDVI for the city and the suburbs¶

Using the two polygons from the last section, use rasterio's mask functionality to create two masked arrays from the landsat data, one for the city and one for the suburbs.

For each masked array, calculate the NDVI.

In [38]:
masked_city, mask_transform = mask(
    dataset=landsat,              # The original raster data
    shapes=city_limits.geometry,  # The vector geometry we want to crop by
    crop=True,                    # Optional: remove pixels not within boundary
    all_touched=True,             # Optional: get all pixels that touch the boudnary
    filled=False,                 # Optional: do not fill cropped pixels with a default value
)
In [39]:
masked_sub, mask_transform = mask(
    dataset=landsat,              # The original raster data
    shapes=suburbs.geometry,  # The vector geometry we want to crop by
    crop=True,                    # Optional: remove pixels not within boundary
    all_touched=True,             # Optional: get all pixels that touch the boudnary
    filled=False,                 # Optional: do not fill cropped pixels with a default value
)
In [40]:
# Note that the indexing here is zero-based, e.g., band 1 is index 0
red_c = masked_city[3]
nir_c = masked_city[4]

red_s = masked_sub[3]
nir_s = masked_sub[4]
In [41]:
def calculate_NDVI(nir, red):
    """
    Calculate the NDVI from the NIR and red landsat bands
    """

    # Convert to floats
    nir = nir.astype(float)
    red = red.astype(float)

    # Get valid entries
    check = np.logical_and(red.mask == False, nir.mask == False)

    # Where the check is True, return the NDVI, else return NaN
    ndvi = np.where(check, (nir - red) / (nir + red), np.nan)
    
    # Return
    return ndvi
In [42]:
NDVI_c = calculate_NDVI(nir_c, red_c)
NDVI_s = calculate_NDVI(nir_s, red_s)
In [43]:
data = landsat.read(1)
In [44]:
# Initialize
fig, ax = plt.subplots(figsize=(10, 10))

# The extent of the data
landsat_extent = [
    landsat.bounds.left,
    landsat.bounds.right,
    landsat.bounds.bottom,
    landsat.bounds.top,
]

# Plot!
img = ax.imshow(data, norm=mcolors.LogNorm(), extent=landsat_extent)

# Add the city limits
city_limits.plot(ax=ax, facecolor="none", edgecolor="white")

# Add a colorbar and turn off axis lines
plt.colorbar(img)
ax.set_axis_off()
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In [46]:
fig, ax = plt.subplots(figsize=(10, 10))

# Plot NDVI
img = ax.imshow(NDVI_c, extent=landsat_extent)

# Format and plot city limits
city_limits.plot(ax=ax, edgecolor="gray", facecolor="none", linewidth=4)
plt.colorbar(img)
ax.set_axis_off()
ax.set_title("NDVI in Philadelphia City", fontsize=18);
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In [45]:
fig, ax = plt.subplots(figsize=(10, 10))

# Plot NDVI
img = ax.imshow(NDVI_s, extent=landsat_extent)

# Format and plot city limits
city_limits.plot(ax=ax, edgecolor="gray", facecolor="none", linewidth=4)
plt.colorbar(img)
ax.set_axis_off()
ax.set_title("NDVI in Philadelphia Suburbs", fontsize=18);
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2.1.4 Calculate the median NDVI within the city and within the suburbs¶

  • Calculate the median value from your NDVI arrays for the city and suburbs
  • Numpy's nanmedian function will be useful for ignoring NaN elements
  • Print out the median values. Which has a higher NDVI: the city or suburbs?
In [47]:
np.nanmedian(NDVI_c)
Out[47]:
0.20268593532493442
In [48]:
np.nanmedian(NDVI_s)
Out[48]:
0.3746654463028859

Suburbs have a higher median NDVI¶

2.2 Calculating the NDVI for Philadelphia's street treets¶

2.2.1 Load the street tree data¶

The data is available in the "data/" folder. It has been downloaded from OpenDataPhilly. It contains the locations of abot 2,500 street trees in Philadelphia.

In [49]:
philly_tree = gpd.read_file("./data/ppr_tree_canopy_points_2015.geojson")

2.2.2 Calculate the NDVI values at the locations of the street trees¶

  • Use the rasterstats package to calculate the NDVI values at the locations of the street trees.
  • Since these are point geometries, you can calculate either the median or the mean statistic (only one pixel will contain each point).
In [50]:
from rasterstats import zonal_stats
In [51]:
# Convert to the landsat CRS
philly_tree = philly_tree.to_crs(epsg=landsat.crs.to_epsg())
# Calculate the zonal statistics
stats_trees = zonal_stats(
    philly_tree, # Vector data as GeoDataFrame 
    NDVI_c, # Raster data as Numpy array
    affine=landsat.transform, # Geospatial info via affine transform
    stats=["mean", "median"]
)

/Users/hangzhao/mambaforge/envs/musa-550-fall-2023/lib/python3.10/site-packages/rasterstats/io.py:328: NodataWarning: Setting nodata to -999; specify nodata explicitly
  warnings.warn(
In [52]:
# Store the median value in the data frame
tree_median_ndvi = [stats_dict["median"] for stats_dict in stats_trees]
philly_tree["median_NDVI"] = tree_median_ndvi

2.2.3 Plotting the results¶

Make two plots of the results:

  1. A histogram of the NDVI values, using matplotlib's hist function. Include a vertical line that marks the NDVI = 0 threshold
  2. A plot of the street tree points, colored by the NDVI value, using geopandas' plot function. Include the city limits boundary on your plot.

The figures should be clear and well-styled, with for example, labels for axes, legends, and clear color choices.

In [53]:
# Initialize
fig, ax = plt.subplots(figsize=(8, 6))

# Plot a quick histogram
ax.hist(philly_tree["median_NDVI"], bins="auto")
ax.axvline(x=0, c="k", lw=2)

# Format
ax.set_xlabel("Median NDVI", fontsize=18)
ax.set_ylabel("tree canopy cover", fontsize=18);
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In [54]:
# Initialize
fig, ax = plt.subplots(figsize=(10, 10))

# Plot the city limits
city_limits.plot(ax=ax, edgecolor="black", facecolor="none", linewidth=4)

# Plot the median NDVI
philly_tree.plot(column="median_NDVI", legend=True, ax=ax, cmap="viridis")

# Format
ax.set_axis_off()
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In [ ]:
# trim to only the columns we want to plot
cols = ["median_NDVI", "geometry"]

# Plot the parks colored by median NDVI
p = philly_tree[cols].hvplot(
    c="median_NDVI", geo=True, crs=32618, cmap="viridis"#, hover_cols=["SITE_NAME"]
)

# Plot the city limit boundary
cl = city_limits.hvplot(
    geo=True,
    crs=32618,
    alpha=0,
    line_alpha=1,
    line_color="black",
    hover=False,
    width=700,
    height=600,
)

# combine!
cl * p